1. Field of the Invention
The present invention relates to field of image processing, and more particularly to a image processing method and an apparatus for removing blur without ringing-artifact in order to restore the image without ringing-artifact.
2. Description of the Related Art
The functions of a mobile communication terminal are typically varied in response to a customer's requirements and/or desires. In addition to a basic call function, the mobile communication terminal may include numerous other functions, such as games, web search, e-mail reception and transmission, and payment, as well as photographing using a camera and TV watching through DMB reception.
Improvements in the quality of the images taken by the camera and the image quality of DMB reception is of significant importance. Particularly, there is a need for a technology for restoring the image without ringing-artifact which often occurs in a procedure of removing blur in order to improve image quality.
The “ringing” phenomenon refers to a condition when an image is reproduced in an image device, such as a TV, vibratory patterns, such as white shadows, occur around the edges of the image. These white shadows at the edges represents an image deviation. Since the ringing phenomenon represents a cause of image quality reduction it is necessary that the ringing phenomenon be attenuated upon reproducing the image.
Conventional method of removing blur, are known in the art. For example, such methods are one of boosting high frequency components, one of using image restoration, and one of a super resolution (SR) method of restoring a high-resolution image using multiple low-resolution images.
In the above-mentioned conventional methods of removing blur, the method of boosting high frequency components has advantages that it is simple and requires less calculations, but has the disadvantage that image quality could not be improved fundamentally. Further, the SR method can basically improve image quality, but has disadvantages that memory is needed to store multiple low-resolution images and required calculations are too large to apply to a system. On the contrary, the method of using image restoration has a relatively small amount of calculations and could remove the blur fundamentally, so it has become widely available in reality.
Image acquisition modeling can be expressed in a matrix-vector models as equation (1) below:y=Hx+n  (1)
where x is an original signal, y is a signal to be obtained and restored, H is blur, and n is noise.
Using equation (1), when a general restoration technique is employed for restoring the original signal x, an estimated x is obtained by equation (2) and equation (3) below:x=inv(H′H+αC′C)H′y,  (2)x(n+1)=x(n)+β(H′y−(H′+H+αC′C)x(n))  (3)where inv(k) represents the inverse matrix of k, α is a regularization parameter, H′ represents a transpose vector of H, and β is an iterative step.
Using equations (2) and (3), it is necessary to obtain an inverse matrix of the blur H in order to obtain an estimated value of an original signal. Here, the product of matrix H and the inverse matrix of H should be an identity matrix. That is, the product of matrix H and inverse matrix of H is an identity matrix refers to that this value corresponds to an all pass filter. However, since H corresponds to a high pass filter, high frequency components of the inverse matrix of H are excessively amplified. However, since H includes high frequency components, there is no inverse matrix of H substantially, or these high frequency components are amplified too much, and thus it causes ringing-artifact. Therefore, a constraint, such as α∥Cx∥^2, is used in order to reduce unnecessary ringing-artifact.
Therefore, as a result of the general image restoration in the form of equations (2) and (3) above, the result of equations (2) and (3) is adjusted by the regularization parameter α. When α becomes relatively small, blur is removed very well, but ringing-artifact becomes increases. Further, there are problems in using the regularization parameter α.
First, since α is not determined automatically, it is too difficult to estimate an α that achieves a best result.
Second, since α is identically applied to the entire image, statistical features of partial images are not reflected properly.
Third, it is difficult to reduce both blur and ringing-artifact by one regularization parameter.
Therefore, it is difficult for both blur removal and ringing-artifact removal to be performed well enough to be satisfied by just only regularization parameter.